Data Maturity and the Challenges Leaders Face to Attain It

gramener's article on the challenges businesses face to attain data maturity
Reading Time: 6 mins

In the previous episode of the Data Science Maturity series, we talked about the 5 levels of data maturity. In this article, we’ll move a step ahead to talk about the challenges that business leaders face to attain data science maturity.

It’s not hard to guess what’s the most essential thing that every organization is aiming at in the new decade – Digital Transformation! The COVID-19 pandemic has hit industries and businesses hard and staying afloat in pandemic times is the need of the hour.

Now since the new normal has driven everything digital, touchless technologies have gained a lot of traction. Therefore, enabling the digital transformation of organizations at every level is crucial to driving operations seamlessly in the current business landscape. This transformation without the effective use of organizational data can make the entire approach spineless.

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Data Maturity Challenges in a Nutshell

Gramener’s Chief Data Scientist, Ganes Kesari, recently hosted a webinar on 5 Steps to Transform into a Data-Driven Organization.

The webinar was attended by many industry experts who wanted to initiate or accelerate the digital transformation in their organizations. The session highlighted how an organization’s data maturity influences its performance. It shed light on how you can assess your data maturity and plan the five steps for data-driven business transformation.

The following infographic is the result of a poll conducted during the webinar where the experts were asked about the biggest data maturity challenges they face.

The Future Lies in a Data-Driven Approach

Data is something that every organization creates at every step of its operations. There is an inflow of large amounts of data from the web, mobile, payment systems, B2B interactions, surveys, app usage, social media, and more. Data is becoming one of the most crucial corporate assets, and some also refer to it as the currency of the 21st century.

When we say digital transformation, we often equate it to the tools or some unique way of working. It runs far more profound – It’s about how you engage your customers remotely, how you take care of your employees, how you manage the supply chains, and how you run your operations for using data and digital as a lifeblood of the organization.

What is Data Maturity?

Data maturity defines the extent to which an organization utilizes its data to drive processes and decisions. Therefore, an organization is more data-mature when it values its data and employs sophisticated techniques for its analysis to gain meaningful insights.

A high level of data maturity comes when data gets seamlessly woven into the fabric and soul of the organization. For this, every step of the decision-making process in the organization should be data-driven.

Organizations worldwide deal with data differently and are at various stages concerning how they perform deep data analysis. Thus, every organization has a unique position on the data maturity curve, and significant differences are present in otherwise similar organizations.

Early adopters and innovators of advanced data maturity reap its benefits and prove to be dominant players in their respective industries. While there are also companies that do little with the data they produce; and they are most likely to be at a very early stage of their data maturity journey.

If your organization is beginning to understand the value of data, check our blog, which will guide you on what is Data Science Maturity and how to assess it in your organization.

Organizations that use data effectively and are at an advanced stage of data maturity can spot several opportunities through insights they receive which generally stay hidden if organizational data is not analyzed correctly. In this respect, predictive analytics is an emerging field that lets organizations understand how the future will look and what measures can guarantee success.

From figuring out the best candidate for a particular job to knowing which products to roll out that will get popular in the customer base, organizations can unearth precious information to help them make the right decision for their businesses.

Thus, leveraging modern analytics techniques gives organizations the power to look ahead instead of relying upon historical data or traditional business intelligence to generate insights.

The journey to attain data maturity is a long process that requires top-down channeling on the importance and value of data. The leadership should make persistent efforts to make data a part of every decision-making process, aligning it with the critical business objectives.

5 Major Data Maturity Challenges Organizations Face

Your organization’s challenges will be very different from any other organization. So, these challenges vary throughout the journey of an organization’s life cycle.

According to a survey that Gartner conducted, 87% of organizations are at low maturity levels.

Let’s understand what the key challenges are that organizations face on the journey to attain data maturity –

Culture and Change Challenge

How do you convince people within an organization to adopt new technology? Cultural change is a significant hindrance in the path upwards. It might be difficult for people to embrace data for decision-making instead of their gut feeling.

Hence, fighting resistance from the people will constitute some of the initial challenges as you embark upon the journey to improve data maturity. It becomes vital to improve the information sharing and data collaboration processes.

Funding Resources Challenge

How do you secure funding for your initiatives? How do you demonstrate that the earlier investments are paying off? That’s where ROI comes into play.

McKinsey says that there are two clear groups which are leaders and laggards. And the gap between them is widening. A small island of companies is extremely good at getting the best business benefits from their data investments. Whereas others see some progress, the benefits from data are not proportionate to that of the industry.

One of the significant responsibilities of D&A leaders is transforming data into a profit center from a cost center. The challenge lies in quantifying the effectiveness of your D&A strategy. How does one quantify the return on investment (ROI) from D&A strategies? It’s an excellent approach to divide the problem into tangible or quantifiable benefits and intangible benefits.

Data Literacy Challenge

We live in a scenario where every industry is accumulating exponential amounts of data. But, while we may have all this data available to us, what is more important is how we get better at interpreting and translating this data into something useful!

Gartner defines data literacy as the ability to read, write and communicate data in context.

Source – Gartner

It is essential to educate your employees in conversing and being comfortable with data. You can’t use data to drive every action until you give every decision-maker the power to access data and the right tools to act on it.

In essence, data should be made available across departments and business users to improve data literacy and enable fact-based decision-making into organizational culture. The best way to improve data literacy is by upskilling knowledge workers and giving them hybrid roles.

People and Skills Challenge

Your organization must possess the people and skills needed to execute the projects it takes up. Foster a data culture in your company by standardizing data sharing and collaboration processes, creating upskilling opportunities for your people, recognizing and rewarding analytics talent, and introducing innovative methods to make the transition easier.

Data Science Strategy Challenge

When leaders across an organization understand the strategic importance of data, it becomes easier to attain data maturity. As your organization progresses to become data mature, the leaders must share the best practices to be followed and act as the forebearers of maturity upscaling.

The data science strategy should help in aligning with business initiatives and picking suitable projects. Big data projects implemented without having a clear business strategy in mind are often dropped mid-way because of low perceived value.

Assessing Your Organization’s Data Science Maturity

Globally, companies are making progress in their efforts to mature their data capabilities. Still, the transformation to data-driven organizations is proving arduous for many — and it is not happening as rapidly as they had hoped. According to BCG’s Data Capability Maturity Survey, the companies that Gramener surveyed in 2016 had expected to raise their data maturity index score by 53% by 2019. But they fell well short of their ambitions, improving their score by 19% according to this year’s survey.

Take our 5-minute Data Science Maturity Assessment (and get a FREE report). Understand where your organization is concerning data maturity. We bring together the best analytics, storytelling, and custom solutions through our low-code platform Gramex to help organizations in their data journey.

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